Conference paper Open Access

Deep-SST-Eddies: A Deep Learning Framework to Detect Oceanic Eddies in Sea Surface Temperature Images

Moschos, Evangelos; Schwander, Olivier; Stegner, Alexandre; Gallinari, Patrick

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<identifier identifierType="URL">https://zenodo.org/record/3898455</identifier>
<creators>
<creator>
<creatorName>Moschos, Evangelos</creatorName>
<givenName>Evangelos</givenName>
<familyName>Moschos</familyName>
</creator>
<creator>
<creatorName>Schwander, Olivier</creatorName>
<givenName>Olivier</givenName>
<familyName>Schwander</familyName>
</creator>
<creator>
<creatorName>Stegner, Alexandre</creatorName>
<givenName>Alexandre</givenName>
<familyName>Stegner</familyName>
</creator>
<creator>
<creatorName>Gallinari, Patrick</creatorName>
<givenName>Patrick</givenName>
<familyName>Gallinari</familyName>
</creator>
</creators>
<titles>
<title>Deep-SST-Eddies: A Deep Learning Framework to Detect Oceanic Eddies in Sea Surface Temperature Images</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2020</publicationYear>
<dates>
<date dateType="Issued">2020-05-01</date>
</dates>
<resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3898455</alternateIdentifier>
</alternateIdentifiers>
<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/icassp40776.2020.9053909</relatedIdentifier>
</relatedIdentifiers>
<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="Abstract">Until now, mesoscale oceanic eddies have been automatically detected through physical methods on satellite altimetry. Nevertheless, they often have a visible signature on Sea Surface Temperature (SST) satellite images, which have not been yet sufficiently exploited. We introduce a novel method that employs Deep Learning to detect eddy signatures on such input. We provide the first available dataset for this task, retaining SST images through altimetric-based region proposal. We train a CNN-based classifier which succeeds in accurately detecting eddy signatures in well-defined examples. Our experiments show that the difficulty of classifying a large set of automatically retained images can be tackled by training on a smaller subset of manually labeled data. The difference in performance on the two sets is explained by the noisy automatic labeling and intrinsic complexity of the SST signal. This approach can provide to oceanographers a tool for validation of altimetric eddy detection through SST.</description>
</descriptions>
</resource>

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